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New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

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Earthquake Engineering and Engineering Vibration 2022年第1期21卷 169-191页

作者： Masoud Haghani Chegeni Mohammad Kazem Sharbatdar Reza Mahjoub Mahdi Raftari Department of Civil Engineering Khorramabad BranchIslamic Azad UniversityKhorramabadIran*** Faculty of Civil Engineering Semnan UniversitySemnanIran***

The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive *** approach sets out to improve current feature extraction techniques in the context of time series *** coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based *** classifiers compute the relative errors in the extracted features between the undamaged and damaged ***,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel *** analyses are performed to validate the superiority of the proposed methods over some existing *** show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based ***,these methods are superior to some classical techniques.

关键词： structural damage diagnosis statistical pattern recognition feature extraction time series analysis supervised learning classification

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COMBINING CLASSIFIERS FOR CREDIT RISK PREDICTION

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Journal of Systems Science and Systems Engineering 2009年第3期18卷 292-311页

作者： Bhekisipho TWALA CSIR Modelling and Digital Sciences Unit P.O. Box 395 Pretoria 0001South Africa

Credit risk prediction models seek to predict quality factors such as whether an individual will default （bad applicant） on a loan or not （good applicant）. This can be treated as a kind of machine learning （ML） problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble （combining） classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how could such accuracy be improved by using pairs of classifier ensembles. Benchmarking results on five credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.

关键词： Supervised learning statistical pattern recognition ensemble credit risk prediction

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